Climate change is intensifying human heat exposure, particularly in densely built urban centers of the Global South. Low-cost construction materials and high thermal-mass surfaces further exacerbate this risk. Yet scalable methods for assessing such heat-relevant building attributes remain scarce. We propose a machine learning framework that fuses openly available unmanned aerial vehicle (UAV) and street-view (SV) imagery via a coupled global context vision transformer (CGCViT) to learn heat-relevant representations of urban structures. Thermal infrared (TIR) measurements from HotSat-1 are used to quantify the relationship between building attributes and heat-associated health risks. Our dual-modality cross-view learning approach outperforms the best single-modality models by up to $9.3\%$, demonstrating that UAV and SV imagery provide valuable complementary perspectives on urban structures. The presence of vegetation surrounding buildings (versus no vegetation), brighter roofing (versus darker roofing), and roofing made of concrete, clay, or wood (versus metal or tarpaulin) are all significantly associated with lower HotSat-1 TIR values. Deployed across the city of Dar es Salaam, Tanzania, the proposed framework illustrates how household-level inequalities in heat exposure - often linked to socio-economic disadvantage and reflected in building materials - can be identified and addressed using machine learning. Our results point to the critical role of localized, data-driven risk assessment in shaping climate adaptation strategies that deliver equitable outcomes.

翻译：气候变化正在加剧人类的热暴露风险，在全球南方人口密集的城市中心尤为突出。低成本建筑材料和高热容表面进一步加剧了这一风险。然而，用于评估此类热相关建筑属性的可扩展方法仍然匮乏。本文提出一种机器学习框架，通过耦合全局上下文视觉Transformer（CGCViT）融合公开可用的无人机（UAV）与街景（SV）图像，以学习城市结构的热相关表征。利用HotSat-1卫星的热红外（TIR）测量数据量化建筑属性与热相关健康风险之间的关系。我们的双模态跨视角学习方法性能优于最佳单模态模型达$9.3\%$，证明无人机与街景图像能为城市结构提供有价值的互补视角。建筑周边植被覆盖（相对于无植被）、浅色屋顶（相对于深色屋顶）以及混凝土、黏土或木质屋顶材料（相对于金属或防水布）均与较低的HotSat-1热红外值显著相关。在坦桑尼亚达累斯萨拉姆全市范围的部署表明，该框架能够识别并应对家庭尺度的热暴露不平等现象——这种不平等常与社会经济劣势相关并体现在建筑材料中。我们的研究结果揭示了本地化、数据驱动的风险评估在制定公平气候适应策略中的关键作用。